Leveraging a rolling cross-section design in panel studies to investigate police legitimacy

Today

Police work requires public support to be able to function in a democratic context
Beliefs that the police act in procedurally just ways and are legitimate facilitate compliance and cooperation with authorities
A socially legitimate police force should require little violence to confront opposition
However, the world has seen many cases of violent demonstrations, police violence and human rights violations

Chile’s main police force, the Carabineros, was among the most trusted institutions (Dammert, 2019)
Yet, recent events of corruption and excessive use of force during the social uprising in October 2019 have produced a serious loss of trust
Crisis of legitimacy: proposals of “deep reform” and even the dissolution of the institution
Need to understand relationship between police and citizens in Chile during the next crucial years

Cross-section: data obtained at a specific point in time
- Limited to correlational research
Causal inference needs to consider time as a factor (Kenski et al., 2010)
Rolling cross-sectional designs (RCS) distribute interviewing within a cross-section in a controlled (random) way over time (Johnston & Brady 2002; Kenski et al., 2010)
- Total sample is divided into multiple replicates (smaller random samples)
- Replicates are assigned to be interviewed at different intervals of times

Time as an opportunity
Can capture naturally-occuring expected and unexpected events
Avoids missatributing effects to the wrong events
Flexibility: information can be divided into different time periods for analysis
Data can be analised as single cross-section, repeated cross-section or aggregated into time series
Little conditioning of participant responses

When populations are studied across an extended period of time, it is assumed that no meaningful variation will take place within the sampling period.
RCS takes advantage of temporal heterogeneity, it’s converted into an object of study
While experiments are considered the best method for establishing causality, they do not consider realistic approaches
Low sample sizes mean high sampling error. Method to alleviate problem is smoothing out the data lines with moving averages, pooling data across days. Noise from sampling error is reduced by capitalising on samples from nearby days (moving averages). However, when data is shifting, mixing values together from different days can mask the shift. Also, centered moving average will reflect a change before the change has actually occurred in the population.

Panel studies: individuals are interviewed at two or more points in time
- Generally better than cross-sectional designs for causal inference (Kenski et al., 2010)
- Possible to make inferences about changes in attitudes or behaviours at the individual level (RCS by itself is not able to capture change)